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Saez-Rodriguez et al. 2009

Discrete logic modeling as a means to link protein signaling networks with functional analysis of mammalian signal transduction
Julio Saez-Rodriguez*, Leonidas G. Alexopoulos*, Jonathan Epperlein, Regina Samaga, Douglas A. Lauffenburger, Steffen Klamt and Peter K. Sorger. Molecular Systems Biology, 5:331, 2009. [* these authors contributed equally to this work]

Large-scale protein signalling networks are useful for exploring complex biochemical pathways but do not reveal how pathways respond to specific stimuli. Such specificity is critical for understanding disease and designing drugs. Here we describe a computational approach–implemented in the free CNO software–for turning signalling networks into logical models and calibrating the models against experimental data. When a literature-derived network of 82 proteins covering the immediate-early responses of human cells to seven cytokines was modelled, we found that training against experimental data dramatically increased predictive power, despite the crudeness of Boolean approximations, while significantly reducing the number of interactions. Thus, many interactions in literature-derived networks do not appear to be functional in the liver cells from which we collected our data. At the same time, CNO identified several new interactions that improved the match of model to data. Although missing from the starting network, these interactions have literature support. Our approach, therefore, represents a means to generate predictive, cell-type-specific models of mammalian signalling from generic protein signalling networks.

The article can be viewed in html or pdf.

This paper presents the tool CellNetOptimizer.

In this paper we used a set of data collected  in HepG2 cells under different conditions (ligands and inhibitors) to train a Boolean model, and then a second set to validate the model. The training data set can be found here, and the validation data set can be downloaded here stored as a MIDAS file that can be loaded and processed with DataRail.

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